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A Bayesian method for the induction of probabilistic networks from data
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  • Published: October 1992

A Bayesian method for the induction of probabilistic networks from data

  • Gregory F. Cooper1 &
  • Edward Herskovits2 

Machine Learning volume 9, pages 309–347 (1992)Cite this article

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  • 2322 Citations

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Abstract

This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.

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Authors and Affiliations

  1. Section of Medical Informatics, Department of Medicine, University of Pittsburgh, B50A Lothrop Hall, 15261, Pittsburgh, PA

    Gregory F. Cooper

  2. Noetic Systems, Incorporated, 2504 Maryland Avenue, 21218, Baltimore, MD

    Edward Herskovits

Authors
  1. Gregory F. Cooper
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  2. Edward Herskovits
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Cooper, G.F., Herskovits, E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9, 309–347 (1992). https://doi.org/10.1007/BF00994110

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  • Issue Date: October 1992

  • DOI: https://doi.org/10.1007/BF00994110

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Keywords

  • probabilistic networks
  • Bayesian belief networks
  • machine learning
  • induction
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